利用BP神经网络对卫星无热敏设备温度的估测

卫星上测温资源有限，只有部分设备有测温点，难以准确获得其他无测温点设备的温度。基于反向传播（BP）神经网络对复杂非线性系统优秀的拟合能力，建立了估测卫星上无测温点设备温度的神经网络，以在轨有测温点设备温度为输入层，以在轨无测温点设备为输出层，并使用卫星热试验获得的星上温度遥测数据和在轨无测温点设备的热电偶温度数据进行训练和测试。测试结果表明，所建立的神经网络估测精度在1℃以内，可以用来精确估测卫星无测温点设备的温度。针对学习样本对估测误差之间关系进行了研究，计算表明，学习样本的多样性和大数据量能够显著减小估测误差。

Temperature estimation of satellite equipment without thermistorbased on BP neural network#br#

There are only a few equipments that can be installed with thermistors because of the limited sources on satellite. A BP neural network which can predict the temperature of equipment without thermistor was built based on the excellent fitting ability of BP neural network for complex nonlinear system. The temperature data acquired in thermal test through thermal couple of equipments on satellite either with or without thermistors were used to train and test the neural network. The test result shows that the temperature prediction accuracy of BP neural network is smaller than 1℃, and the temperature prediction neural network can be used to accurately predict the temperature of equipment without thermistor. Additionally, the relationship between samples and estimation errors were also studied, showing that sample diversity and large data can reduce the estimation error significantly.